Does a neural residual help a quadrotor attitude controller once roll, pitch and yaw are coupled?

What it found

Follow-up to codesign-aero-skip: roll, pitch and yaw with Euler gyroscopic coupling and a weak yaw axis. On six airframes × five CMA-ES seeds, held-out combined RMS attitude error is 17.76° for a 576-evaluation per-axis PID, 18.08° for PID plus an 83-parameter zero-initialised residual network (PID better in 26 of 30 pairs), and 18.20° for the 96-evaluation PID with or without analytic gyroscopic compensation (difference −0.001°). A 12-condition sweep over Iz/Ix (1.0–3.0), manoeuvre size and yaw authority finds the PID ahead of the residual in all 12 and compensation worth at most 0.016°, even where the gyroscopic term peaks at 71% of the torque limit. Yaw (about 29° RMS on 30° steps) is authority-limited and dominates the error; tripling yaw authority helps by 1.8°, the control-law class does not. Limits: the yaw reaction-torque ratio, inertia ratio and plant ranges are assumed and swept, not measured; attitude only with step references; the residual overfits 16 training episodes and a different learner might differ.

A figure from its own output

Paper, code and results

• README and method
• Experiments and results
• Code
• Tests

More in Agentic co-design of low-cost robots

• Does a small neural roll controller beat the three-gain PID on a randomised quadrotor, and does it change which airframe is best?Does a 31-parameter neural roll controller beat the three-gain PID on a randomised quadrotor? Not at equal evaluations (7.7° PID against 9.0°); with 10× the evaluations it only matches it (7.6°), and the airframe ranking is unchanged (Spearman 1.00).
• Does a neural residual on top of the tuned PID get a quadrotor roll controller below the PID error floor?Does a zero-initialised neural residual on the tuned PID beat the PID on a randomised quadrotor? No at equal evaluations (7.68° against 7.58°, better in 7 of 18 pairs); the airframe ranking is unchanged (Spearman 1.00).
• Does the strawberry gripper co-design survive the measured range of stem pull-off force?Does the strawberry-gripper co-design of codesign-agri survive measured stem pull-off forces (3.17 N bending and pulling, 13.94 N pulling alone) instead of an assumed 3.5 N? Partly: the optimiser ranking holds in 8 of 10 cells, but the best achievable objective falls from 0.665 to 0.075 as the median pull-off rises from 3.17 to 13.94 N, and at 13.94 N no 40-evaluation run finds a gripper that picks anything
• Do the codesign-agri conclusions survive when its assumed constants change?Does the optimiser ranking of codesign-agri survive a sweep of its 12 invented constants? Mostly: Bayesian optimisation beats CMA-ES beats random search in 82 of 100 worlds, but the best gripper has 2, 3 or 4 fingers depending on stem strength and the nominal winner loses 0.06 J or goes over budget in most worlds
• Does diagnostic feedback help a gripper design loop once the task has headroom?Does diagnostic feedback help a gripper design loop once the scene generator is fixed? Still no: every method gains only 0.00 to 0.05 (best test success 0.85 to 0.90) and the failure-summary and rollout-log readers do not beat random search, CMA-ES or Bayesian optimisation (stylised, rule-based readers, not an LLM).
• Which gripper co-design method finds the best design when designs are scored on 600 scenes instead of 40?Does the ranking of co-design methods survive 600 test scenes? No: re-scoring the 144 final designs of codesign-headroom gives 0.82 to 0.85 true success for every method, random search ties CMA-ES and Bayesian optimisation, and the rollout-log reader trails by 0.03 (interval includes 0)

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Proper scoring & elicitation28Effort & peer prediction9Verification games & slashing19Detection & tolerances14Markets as routers13Prediction-market design9Paying contributors8Decentralised training systems23Digital twins & sim-to-real164LLM chip design loops40Robot manufacturing loops53Ambient AI assistant46Self-replicating manufacturing34Simulation-first design64Engineered clothing45Task-specific AI accelerators40Algorithmic chip-design loops42Open-source purpose-built robots44The software-engineering loop43Adversarial human imitation41Open-source residential construction41Autonomous multi-angle capture34Sensing, imaging and 3D31Energy supply and dense energy storage39Robot societies among people31Agentic co-design of low-cost robots27Laundry-hanging robots25Machine learning on qualitative data23Robots on the building site27Civilizations & group psychology1History & human sciences1

Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.